Real-time nowcasting Growth-at-Risk using the Survey of Professional Forecasters

This paper investigates nowcasting Growth-at-Risk (GaR) using consensus forecasts from the Survey of Professional Forecasters (SPF) in the US. Incorporating SPF consensus forecasts into the conditional mean of an AR-GARCH type model significantly enhances nowcasting accuracy for GaR and the conditional density of GDP growth. While there is strong time variation in both the lower and upper quantiles of the GDP growth distribution, integrating skewness and fat tails into the model does not improve forecasting accuracy. By accounting for changes in the conditional mean of the GDP growth distribution over time, these findings highlight the value of SPF consensus projections for GaR nowcasting.